for Forecasting Infrastructure
3.2.1 Personal Computers Network Infrastructure
3.2.2 Client/Server Infrastructure
3.2.3 Cloud Computing Infrastructure
3.3.1 Data Collection Software
3.3.2 Data Preparation Software
3.3.5 Software Selection Criteria
3.4.1 Internal Data Infrastructure
3.4.2 External Data Infrastructure
3.5 Organizational Infrastructure
3.5.1 Developers Infrastructure
3.5.3 Work Process Implementation
Chapter 4 Issues with Data Mining for Forecasting Application
4.2 Technical Issues
4.2.1 Data Quality Issues
4.2.2 Data Mining Methods Limitations
4.2.3 Forecasting Methods Limitations
4.3 Nontechnical Issues
4.3.1 Managing Forecasting Expectations
4.3.2 Handling Politics of Forecasting
4.3.3 Avoiding Bad Practices
4.3.4 Forecasting Aphorisms
4.4 Checklist “Are We Ready?”
5.1 Introduction
5.2 System Structure and Data Identification
5.2.1 Mind-Mapping
5.2.2 System Structure Knowledge Acquisition
5.2.3 Data Structure Identification
5.3 Data Definition
5.3.1 Data Sources
5.3.2 Metadata
5.4 Data Extraction
5.4.1 Internal Data Extraction
5.4.2 External Data Extraction
5.5 Data Alignment
5.5.1 Data Alignment to a Business Structure
5.5.2 Data Alignment to Time
5.6 Data Collection Automation for Model Deployment
5.6.1 Differences between Data Collection for Model Development and Deployment
5.6.2 Data Collection Automation for Model Deployment
6.1 Overview
6.2 Transactional Data Versus Time Series Data
6.3 Matching Frequencies
6.3.1 Contracting
6.3.2 Expanding
6.4 Merging
6.5 Imputation
6.6 Outliers
6.7 Transformations
6.8 Summary
Chapter 7 A Practitioner's Guide of DMM Methods for Forecasting
7.1 Overview
7.2 Methods for Variable Reduction
Traditional Data Mining
Time Series Approach
7.3 Methods for Variable Selection
Traditional Data Mining
Example for Variable Selection
Variable Selection Based on Pearson Product-Moment Correlation
